2011电工杯数学建模竞赛A题，风功率数据预测问题，题目和附件数据完整。

计算脉动风时程曲线，可利用规范中的功率谱密度函数生成

随着新能源的不断发展，大量大容量风电机组并入电网运行，给电网的安全可靠运行以及风力发电的可持续发展都提出了新的挑战。提出一种风功率预测模型，该模型以风电场风功率历史数据以及风速、风向等数值天气预报数据作为输入对风功率进行预测。考虑到风功率预测中输入数据的波动性和不确定性，在传统门控循环单元(GRU)神经网络的基础上融合卷积神经网络(CNN)，以提高模型对原始数据的特征提取和降维能力，并引入dropout技术减少模型中的过拟合现象。工程实例分析表明，所提模型在预测准确度和运算速度方面均优于长短记忆神经网络模型。

行业分类-电子电器-一种计及风功率波动的电力系统惯量需求评估的方法.zip

【本科毕设】基于matlab的风功率预测模型及程序.rar f

matlab python 风功率预测 机器学习 深度学习

直接法求脉动风功率谱密度函数，同时适用于其他功率谱密度函数的求法

In this paper, we investigate the representation of wind power forecasting (WPF) uncertainty in the unit commitment (UC) problem. While deterministic approaches use a point forecast of wind power output, WPF uncertainty in the stochastic UC alternative is captured by a number of scenarios that include crosstemporal dependency. A comparison among a diversity of UC strategies (based on a set of realistic experiments) is presented. The results indicate that representing WPF uncertainty with wind power scenarios that rely on stochastic UC has advantages over deterministic approaches that mimic the classical models. Moreover, the stochastic model provides a rational and adaptive way to provide adequate spinning reserves at every hour, as opposed to increasing reserves to predefined, fixed margins that cannot account either for the system’s costs or its assumed risks.